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Concept

Clustering high-dimensional data

Related lectures (31)

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

Clustering: Unsupervised Learning

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.

Single cell RNA-sequencing: Methods and Applications

Explores single cell RNA-sequencing methods, applications, tissue studies, and data clustering.

Decision Making Under Uncertainty

Explores decision-making under uncertainty, focusing on Kilian Schindler's posthumous PhD thesis on scalable stochastic optimization and scenario reduction.

Unsupervised Learning: Clustering Methods

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

Animal Behavior Dynamics: Hidden Insights and Predictive Models

Explores hidden dynamics in animal behavior, emphasizing the importance of short timescale behavior for predicting long timescale dynamics.

Support Vector Clustering: SVC

Introduces Support Vector Clustering (SVC) using a Gaussian kernel for high-dimensional feature space mapping and explains its constraints and Lagrangian.

Clustering Methods: K-means and DBSCAN

Explores K-means and DBSCAN clustering methods, discussing properties, drawbacks, initialization, and optimal cluster selection.

Semi-supervised Clustering: Transductive SVM

Explores Transductive Support Vector Machine for semi-supervised clustering, aiming for zero error on labeled points and well-separated unlabeled points.

Single cell RNA-sequencing: Methods and Data Analysis

Covers the process of single cell RNA-sequencing and clustering data.

Machine Learning: Types and Applications

Covers the types of machine learning, including supervised, unsupervised, and reinforcement learning.

Gaussian Mixture Clustering: Expectation-Maximization Algorithms

Explores the Expectation-Maximization algorithm for Gaussian Mixture Clustering and its challenges and practical implementation.

K-Means Clustering: Basics and Applications

Introduces K-Means Clustering, a simple yet effective algorithm for grouping data points into clusters.

Unsupervised Machine Learning: Clustering Basics

Introduces unsupervised machine learning clustering techniques like K-means, Gaussian Mixture Models, and DBSCAN, explaining their algorithms and applications.

Soft K-means Clustering & DBSCAN

Covers Soft K-means Clustering and DBSCAN principles, algorithms, and comparison.

Statistical Analysis of Networks: Link Prediction and Biclustering Log in to Mediaspace to watch this video

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.

Clustering Techniques: K-means and DBSCAN Log in to Mediaspace to watch this video

Explores k-means and DBSCAN clustering techniques, covering data point assignment and classification types.

Resource Management: Static vs Resource Containers in YARN Log in to Mediaspace to watch this video

Explores static resource allocation and resource containers in distributed computing frameworks, emphasizing the flexibility and efficiency brought by YARN.

Extensions to Exchangeability: Statistical Analysis of Network Data Log in to Mediaspace to watch this video

Covers extensions to exchangeability in network data analysis, including graphon frameworks and edge exchangeability.

The Gamma Parallel Database Machine: Data Clustering and Declustering Log in to Mediaspace to watch this video

Explores The Gamma Parallel Database Machine, data clustering, and declustering tradeoffs.